Development and validation of a numerical tool for the simulation of the temperature field and infrared radiance rendering in an urban scene

International audienceWe present a numerical tool aimed at simulating infrared images of an urban environment, by solving the direct heat transfer problem, and then computing the radiance rendering at the sensor level. SOLENE (Cerma, Nantes) was coupled with two software packages developed at ONERA: SUSHI (Simulation in Urban Scene of Heat dIffusion) and MOHICANS (MOdélisation Hyperspectrale d'Images en entrée Capteur pour l'ANalyse et l'inversion du Signal) for realizing this task. SUSHI is also used for computing the surface temperatures: either a 1D model or a 2D model is used. We present the whole software chain, its validation by software and experimental analysis

The CEOS Feasibility Study for an aquatic ecosystem imaging spectrometer

The Committee on Earth Observation Satellites (CEOS) response to the Group on Earth Observations System of Systems (GEOSS) Water Strategy developed under the auspices of the Water Strategy Implementation Study Team was endorsed by CEOS at the 2015 Plenary. As one of the actions, CSIRO has taken the lead on recommendation C.10: A feasibility assessment to determine the benefits and technological difficulties of designing a hyperspectral satellite mission focused on water quality measurements. More specifically this report is a highlevel feasibility assessment of the benefits and technological difficulties of designing a hyperspectral satellite mission focused on biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macroalgae , seagrasses and coral reefs at significantly higher spatial resolution than 250 m, which is the maximum spatial resolution of dedicated current aquatic sensors such as Sentinel3 and future planned aquatic sensors such as the Coastal Ocean Color Imager (COCI – 100 m res). Further, the GEO Community of Practice Aquawatch suggested that alternative approaches, involving augmenting designs of spaceborne sensors for terrestrial and ocean colour applications to allow improved inland, near coastal waters and benthic applications, could offer an alternative pathway to addressing the same underlying science questions. Accordingly, this study also analizes the benefits and technological difficulties of this option as part of the highlevel feasibility study

Estimation of Soil Moisture Content on Spectral Reflectance of Bare Soils in the 0.4 -2.5 µm Domain

International audienceThe purpose of this paper is to define a method of soil moisture content estimation for bare soils based on spectral signature in the reflective domain. In a first time, this work consists in defining the most performant and robust methods existing in the literature. Two spectral indexes are then retained. In a second time, new methods are proposed to overcome the limitation of these selected indexes. Two spectral indexes and two global methods are then defined. The first method is based on a global method found in the literature and the second method is based on an existing empirical soil model. All these methods are compared on a reference database composed of spectral signature measured in laboratory and related soil moisture content. Finally, a simulation of a realistic case in order to analyse the impact of a given hyperspectral instrument specification and of the atmospheric water vapour content on the methods

Extraction of optimal spectral bands using hierarchical band merging out of hyperspectral data

International audienceSpectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspec-tral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands

ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas

International audienceMany applications dedicated to urban areas (e.g. land cover mapping and biophysical properties estimation) using high spatial resolution remote sensing images require the use of 3D atmospheric correction methods, able to model complex light interactions within urban topography such as buildings and trees. Currently, one major drawback of these methods is their lack in modelling the radiative signature of trees (e.g. the light transmitted through the tree crown), which leads to an over-estimation of ground reflectance at tree shadows. No study has been carried out to take into account both optical and structural properties of trees in the correction provided by these methods. The aim of this work is to improve an existing 3D atmospheric correction method, ICARE (Inversion Code for urban Areas Reflectance Extraction), to account for trees in its new version, ICARE-VEG (ICARE with VEGetation). After the execution of ICARE, the methodology of ICARE-VEG consists in tree crown delineation and tree shadow detection, and then the application of a physics-based correction factor in order to perform a tree-specific local correction for each pixel in tree shadow. A sensitivity analysis with a design of experiments performed with a 3D canopy radiative transfer code, DART (Discrete Anisotropic Radiative Transfer), results in fixing the two most critical variables contributing to the impact of an isolated tree crown on the radiative energy budget at tree shadow: the solar zenith angle and the tree leaf area index (LAI). Thus, the approach to determine the correction factor relies on an empirical statistical regression and the addition of a geometric scaling factor to account for the tree crown occultation from ground. ICARE-VEG and ICARE performance were compared and validated in the Visible-Near Infrared Region (V-NIR: 0.4-1.0µm) with hyperspectral airborne data at 0.8m resolution on three ground materials types, grass, asphalt and water. Results show that (i) ICARE-VEG improves the mean absolute error in retrieved reflectances compared to ICARE in tree shadows by a multiplicative factor ranging between 4.2 and 18.8, and (ii) reduces the spectral bias in reflectance from visible to NIR (due to light transmission through the tree crown) by a multiplicative factor between 1.0 and 1.4 in terms of spectral angle mapper performance. ICARE-VEG opens the way to a complete interpretation of remote sensing images (sunlit, shade cast by both buildings and trees) and the derivation of scientific value-added products over all the entire image without the preliminary step of shadow masking.De nombreuses applications dans les zones urbaines (par exemple la cartographie de la couverture terrestre et l'estimation des propriétés biophysiques) utilisant des images à haute résolution spatiale et à télédétection nécessitent l'utilisation de méthodes de correction atmosphérique 3D. Actuellement, un inconvénient majeur de ces méthodes est leur manque de modélisation de la signature rayonnante des arbres, ce qui conduit à une surestimation de la réflectance au sol à l'ombre des arbres. Aucune étude n'a été faite pour prendre en compte ces propriétés optiques et structurelles des arbres dans la correction apportée par ces méthodes. L'ICARE (code d'inversion pour l'extraction de réflectance des zones urbaines), ICARE-VEG (ICARE avec VEGetation). Après l'exécution d'ICARE, la méthodologie d'ICARE-VEG consiste en un arbre et un arbre, puis l'application d'un facteur de correction basé sur la physique dans une correction locale spécifique à l'arbre pour chaque pixel dans l'ombre de l'arbre. DART (Discrete Anisotropic Radiative Transfer), DART (Transfert Radiatif Anisotrope Discret), permet de déterminer les deux variables les plus importantes contribuant à l'impact d'un arbre isolé sur le bilan énergétique radiatif à l'ombre des arbres: l'angle zénithal solaire et la surface foliaire index (LAI). Ainsi, l'approche du facteur de correction est basée sur une régression statistique empirique et l'ajout d'un facteur d'échelle géométrique au compte de la dissimulation de l'arbre à partir du sol. Les performances ICARE-VEG et ICARE ont été comparées et validées dans la région infrarouge proche visible (V-NIR: 0.4-1.0μm) avec des données aéroportées hyperspectrales à une résolution de 0.8m sur trois types: gazon, asphalte et eau. Les résultats montrent que (i) ICARE-VEG améliore l'erreur absolue moyenne dans les réflectances récupérées par rapport à ICARE dans les ombres portées par un facteur multiplicatif compris entre 4,2 et 18,8, et (ii) réduit le biais spectral de réflectance du visible au NIR (dû à ICARE-VEG ouvre la voie à une interprétation complète des images de télédétection et à la dérivation de produits scientifiques à valeur ajoutée de partout dans le monde, grâce à un facteur multiplicatif compris entre 1,0 et 1,4 en termes de performance de la carte de l'angle spectral. ICARE-VEG ouvre la voie à une interprétation complète des images de télédétection et à la dérivation de produits scientifiques à valeur ajoutée de partout dans le monde

Sensor radiance physical model for rugged heterogeneous surfaces in the 3-14 µm region

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SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) to material classification was also shown

RECONSTRUCTION OF SKY ILLUMINATION DOMES FROM GROUND-BASED PANORAMAS

The knowledge of the sky illumination is important for radiometric corrections and for computer graphics applications such as relighting or augmented reality. We propose an approach to compute environment maps, representing the sky radiance, from a set of ground-based images acquired by a panoramic acquisition system, for instance a mobile-mapping system. These images can be affected by important radiometric artifacts, such as bloom or overexposure. A Perez radiance model is estimated with the blue sky pixels of the images, and used to compute additive corrections in order to reduce these radiometric artifacts. The sky pixels are then aggregated in an environment map, which still suffers from discontinuities on stitching edges. The influence of the quality of estimated sky radiance on the simulated light signal is measured quantitatively on a simple synthetic urban scene; in our case, the maximal error for the total sensor radiance is about 10%